Paper: arXiv 2602.10960

Authors: Ilias Aarab, Thomas Gottron, Andrea Colombo, Jörg Reddig, Annalauro Ianiro

Abstract

Micro-structural models of contagion and systemic risk emphasize that shock propagation is inherently multi-channel, spanning counterparty exposures, short-term funding and roll-over risk, securities cross-holdings, and common-asset (fire-sale) spillovers. Empirical implementations, however, often rely on stylized or simulated networks, or focus on a single exposure dimension, reflecting the practical difficulty of reconciling heterogeneous granular collections into a coherent representation with consistent identifiers and consolidation rules. We close part of this gap by constructing an empirically grounded multilayer network for euro area significant banking groups that integrates several supervisory and statistical datasets into layer-consistent exposure matrices defined on a common node set. Each layer corresponds to a distinct transmission channel, long- and short-term credit, securities cross-holdings, short-term secured funding, and overlapping external portfolios, and nodes are enriched with balance-sheet information to support model calibration. We document pronounced cross-layer heterogeneity in connectivity and centrality, and show that an aggregated (flattened) representation can mask economically relevant structure and misidentify the institutions that are systemically important in specific markets. We then illustrate how the resulting network disciplines standard systemic-risk analytics by implementing a centrality-based propagation measure and a micro-structural agent-based framework on real exposures. The approach provides a data-grounded basis for layer-aware systemic-risk assessment and stress testing across multiple dimensions of the banking network.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper excels in empirical rigor by constructing a highly complex, multi-layer dataset from real granular European banking collections (AnaCredit, SHSG, SFTDS, etc.) to assess systemic risk. While the mathematical formulations are based on standard graph theory and micro-structural agent-based models rather than new theoretical proofs, its data integration novelty and real-world application provide strong practical value. Reproducibility is limited by the usage of highly restricted, proprietary supervisory datasets.

Research Flowchart

  flowchart TD
    A["Research Goal: Assess Euro Area Systemic Risk via Multilayer Network"] --> B["Data Integration & Harmonization<br><i>(Supervisory & Statistical Datasets)</i>"]
    B --> C["Construct 5-Layer Network Architecture<br><i>(Credit, Securities, Secured Funding, Portfolios)</i>"]
    C --> D["Network Analysis & Modeling<br><i>(Centrality Metrics & Agent-Based Simulation)</i>"]
    D --> E["Findings: Layer Heterogeneity & Flattened Networks Mask Risk"]
    D --> F["Outcomes: Enhanced Systemic Risk & Stress Testing Framework"]

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1 of 63 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.